import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from datetime import datetime

plt.style.use('ggplot')
pd.set_option('display.max_columns', None)

from utils import describe_dataset
# 1.1 Carga y descripción de los datos
electrical_data = pd.read_csv('electrical_data.csv')
environment_data = pd.read_csv('environment_data.csv')
irradiance_data = pd.read_csv('irradiance_data.csv')

irradiance_data = pd.read_csv("irradiance_data.csv", parse_dates=["measured_on"])

for col in irradiance_data.columns:
    if col != "measured_on":
        irradiance_data[col] = pd.to_numeric(irradiance_data[col], errors="coerce")

describe_dataset(electrical_data, 'Electrical Data')
describe_dataset(environment_data, 'Environment Data')
describe_dataset(irradiance_data, 'Irradiance Data')

/home/chris/.local/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

==================================================
Descripción del dataset: Electrical Data
==================================================
Número de filas: 632952
Número de columnas: 120

Primeras 5 filas:

Resumen de tipos de datos y valores faltantes:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 632952 entries, 0 to 632951
Columns: 120 entries, measured_on to inv_24_ac_power_inv_149698
dtypes: float64(119), object(1)
memory usage: 579.5+ MB

None

Estadísticas descriptivas:

==================================================
Descripción del dataset: Environment Data
==================================================
Número de filas: 206008
Número de columnas: 4

Primeras 5 filas:

Resumen de tipos de datos y valores faltantes:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 206008 entries, 0 to 206007
Data columns (total 4 columns):
 #   Column                        Non-Null Count   Dtype  
---  ------                        --------------   -----  
 0   measured_on                   206008 non-null  object 
 1   ambient_temperature_o_149575  205876 non-null  object 
 2   wind_speed_o_149576           205992 non-null  object 
 3   wind_direction_o_149577       206000 non-null  float64
dtypes: float64(1), object(3)
memory usage: 6.3+ MB

None

Estadísticas descriptivas:

==================================================
Descripción del dataset: Irradiance Data
==================================================
Número de filas: 531019
Número de columnas: 2

Primeras 5 filas:

from utils import analyze_structure 

# 1.2 Estructura de los conjuntos de datos
electrical_data = analyze_structure(electrical_data, "Electrical Data")
environment_data = analyze_structure(environment_data, "Environment Data")
irradiance_data = analyze_structure(irradiance_data, "Irradiance Data")

Análisis de estructura para: Electrical Data

Columnas de fecha/hora identificadas: ['measured_on']

Rango temporal:
Fecha mínima: 2017-11-01 00:00:00
Fecha máxima: 2023-11-07 23:55:00
Duración total: 2197 days 23:55:00

Columnas numéricas (119):
['inv_01_dc_current_inv_149579', 'inv_01_dc_voltage_inv_149580', 'inv_01_ac_current_inv_149581', 'inv_01_ac_voltage_inv_149582', 'inv_01_ac_power_inv_149583', 'inv_02_dc_current_inv_149584', 'inv_02_dc_voltage_inv_149585', 'inv_02_ac_current_inv_149586', 'inv_02_ac_voltage_inv_149587', 'inv_02_ac_power_inv_149588', 'inv_03_dc_current_inv_149589', 'inv_03_dc_voltage_inv_149590', 'inv_03_ac_current_inv_149591', 'inv_03_ac_voltage_inv_149592', 'inv_03_ac_power_inv_149593', 'inv_04_dc_current_inv_149594', 'inv_04_dc_voltage_inv_149595', 'inv_04_ac_current_inv_149596', 'inv_04_ac_voltage_inv_149597', 'inv_04_ac_power_inv_149598', 'inv_05_dc_current_inv_149599', 'inv_05_ac_current_inv_149601', 'inv_05_ac_voltage_inv_149602', 'inv_05_ac_power_inv_149603', 'inv_06_dc_current_inv_149604', 'inv_06_dc_voltage_inv_149605', 'inv_06_ac_current_inv_149606', 'inv_06_ac_voltage_inv_149607', 'inv_06_ac_power_inv_149608', 'inv_07_dc_current_inv_149609', 'inv_07_dc_voltage_inv_149610', 'inv_07_ac_current_inv_149611', 'inv_07_ac_voltage_inv_149612', 'inv_07_ac_power_inv_149613', 'inv_08_dc_current_inv_149614', 'inv_08_dc_voltage_inv_149615', 'inv_08_ac_current_inv_149616', 'inv_08_ac_voltage_inv_149617', 'inv_08_ac_power_inv_149618', 'inv_09_dc_current_inv_149619', 'inv_09_dc_voltage_inv_149620', 'inv_09_ac_current_inv_149621', 'inv_09_ac_voltage_inv_149622', 'inv_09_ac_power_inv_149623', 'inv_10_dc_current_inv_149624', 'inv_10_dc_voltage_inv_149625', 'inv_10_ac_current_inv_149626', 'inv_10_ac_voltage_inv_149627', 'inv_10_ac_power_inv_149628', 'inv_11_dc_current_inv_149629', 'inv_11_dc_voltage_inv_149630', 'inv_11_ac_current_inv_149631', 'inv_11_ac_voltage_inv_149632', 'inv_11_ac_power_inv_149633', 'inv_12_dc_current_inv_149634', 'inv_12_dc_voltage_inv_149635', 'inv_12_ac_current_inv_149636', 'inv_12_ac_voltage_inv_149637', 'inv_12_ac_power_inv_149638', 'inv_13_dc_current_inv_149639', 'inv_13_dc_voltage_inv_149640', 'inv_13_ac_current_inv_149641', 'inv_13_ac_voltage_inv_149642', 'inv_13_ac_power_inv_149643', 'inv_14_dc_current_inv_149644', 'inv_14_dc_voltage_inv_149645', 'inv_14_ac_current_inv_149646', 'inv_14_ac_voltage_inv_149647', 'inv_14_ac_power_inv_149648', 'inv_15_dc_current_inv_149649', 'inv_15_dc_voltage_inv_149650', 'inv_15_ac_current_inv_149651', 'inv_15_ac_voltage_inv_149652', 'inv_15_ac_power_iinv_149653', 'inv_16_dc_current_inv_149654', 'inv_16_dc_voltage_inv_149655', 'inv_16_ac_current_inv_149656', 'inv_16_ac_voltage_inv_149657', 'inv_16_ac_power_inv_149658', 'inv_17_dc_current_inv_149659', 'inv_17_dc_voltage_inv_149660', 'inv_17_ac_current_inv_149661', 'inv_17_ac_voltage_inv_149662', 'inv_17_ac_power_inv_149663', 'inv_18_dc_current_inv_149664', 'inv_18_dc_voltage_inv_149665', 'inv_18_ac_current_inv_149666', 'inv_18_ac_voltage_inv_149667', 'inv_18_ac_power_inv_149668', 'inv_19_dc_current_inv_149669', 'inv_19_dc_voltage_inv_149670', 'inv_19_ac_current_inv_149671', 'inv_19_ac_voltage_inv_149672', 'inv_19_ac_power_inv_149673', 'inv_20_dc_current_inv_149674', 'inv_20_dc_voltage_inv_149675', 'inv_20_ac_current_inv_149676', 'inv_20_ac_voltage_inv_149677', 'inv_20_ac_power_inv_149678', 'inv_21_dc_current_inv_149679', 'inv_21_dc_voltage_inv_149680', 'inv_21_ac_current_inv_149681', 'inv_21_ac_voltage_inv_149682', 'inv_21_ac_power_inv_149683', 'inv_22_dc_current_inv_149684', 'inv_22_dc_voltage_inv_149685', 'inv_22_ac_current_inv_149686', 'inv_22_ac_voltage_inv_149687', 'inv_22_ac_power_inv_149688', 'inv_23_dc_current_inv_149689', 'inv_23_dc_voltage_inv_149690', 'inv_23_ac_current_inv_149691', 'inv_23_ac_voltage_inv_149692', 'inv_23_ac_power_inv_149693', 'inv_24_dc_current_inv_149694', 'inv_24_dc_voltage_inv_149695', 'inv_24_ac_current_inv_149696', 'inv_24_ac_voltage_inv_149697', 'inv_24_ac_power_inv_149698']

Columnas categóricas (0):
[]

Análisis de estructura para: Environment Data

Columnas de fecha/hora identificadas: ['measured_on']

Rango temporal:
Fecha mínima: 2017-12-01 00:00:00
Fecha máxima: 2023-10-31 23:45:00
Duración total: 2160 days 23:45:00

Columnas numéricas (1):
['wind_direction_o_149577']

Columnas categóricas (2):
['ambient_temperature_o_149575', 'wind_speed_o_149576']

Análisis de estructura para: Irradiance Data

Columnas de fecha/hora identificadas: ['measured_on']

Rango temporal:
Fecha mínima: 2017-11-01 07:10:00
Fecha máxima: 2023-11-01 23:55:00
Duración total: 2191 days 16:45:00

Columnas numéricas (1):
['poa_irradiance_o_149574']

Columnas categóricas (0):
[]

from utils import check_missing_duplicates

# 1.3 Identificación de valores faltantes y duplicados
check_missing_duplicates(electrical_data, "Electrical Data")
check_missing_duplicates(environment_data, "Environment Data")
check_missing_duplicates(irradiance_data, "Irradiance Data")

==================================================
Análisis de valores faltantes y duplicados: Electrical Data
==================================================

Valores faltantes por columna:

Número de filas duplicadas: 0

Análisis de ceros e infinitos en columnas numéricas:

==================================================
Análisis de valores faltantes y duplicados: Environment Data
==================================================

Valores faltantes por columna:

Número de filas duplicadas: 0

Análisis de ceros e infinitos en columnas numéricas:

==================================================
Análisis de valores faltantes y duplicados: Irradiance Data
==================================================

Valores faltantes por columna:

Número de filas duplicadas: 0

Análisis de ceros e infinitos en columnas numéricas:

from utils import descriptive_stats

# 2.1 Estadísticas descriptivas para variables numéricas
descriptive_stats(electrical_data, "Electrical Data")
descriptive_stats(environment_data, "Environment Data")
descriptive_stats(irradiance_data, "Irradiance Data")

==================================================
Estadísticas descriptivas detalladas: Electrical Data
==================================================

==================================================
Estadísticas descriptivas detalladas: Environment Data
==================================================

==================================================
Estadísticas descriptivas detalladas: Irradiance Data
==================================================

# 2.2 Visualización de variables continuas (histogramas)

from utils import plot_histograms

plot_histograms(electrical_data, "Electrical Data")
plot_histograms(environment_data, "Environment Data")
plot_histograms(irradiance_data, "Irradiance Data")

from utils import plot_categorical 

# 2.3 Visualización de variables categóricas (gráfico de barras)
plot_categorical(electrical_data, "Electrical Data")
plot_categorical(environment_data, "Environment Data")
plot_categorical(irradiance_data, "Irradiance Data")

from utils import plot_correlations

# 2.4 Análisis de correlaciones
plot_correlations(electrical_data, "Electrical Data")
plot_correlations(environment_data, "Environment Data")
plot_correlations(irradiance_data, "Irradiance Data")

Correlaciones fuertes (>0.7 o <-0.7):

inv_12_ac_current_inv_149636  inv_12_ac_power_inv_149638      0.999389
inv_12_ac_power_inv_149638    inv_12_ac_current_inv_149636    0.999389
inv_06_ac_power_inv_149608    inv_06_ac_current_inv_149606    0.999371
inv_06_ac_current_inv_149606  inv_06_ac_power_inv_149608      0.999371
inv_15_ac_current_inv_149651  inv_15_ac_power_iinv_149653     0.999371
                                                                ...   
inv_17_dc_current_inv_149659  inv_07_ac_current_inv_149611    0.806824
inv_20_ac_current_inv_149676  inv_22_dc_voltage_inv_149685    0.702169
inv_22_dc_voltage_inv_149685  inv_20_ac_current_inv_149676    0.702169
inv_01_ac_current_inv_149581  inv_01_ac_voltage_inv_149582    0.702125
inv_01_ac_voltage_inv_149582  inv_01_ac_current_inv_149581    0.702125
Length: 6904, dtype: float64

No hay suficientes variables numéricas para calcular correlaciones en Environment Data
No hay suficientes variables numéricas para calcular correlaciones en Irradiance Data

from utils import analyze_temporal_patterns

# 3.1 Identificación de patrones temporales
electrical_data_temp = analyze_temporal_patterns(electrical_data, "Electrical Data")
environment_data_temp = analyze_temporal_patterns(environment_data, "Environment Data")
irradiance_data_temp = analyze_temporal_patterns(irradiance_data, "Irradiance Data")

==================================================
Análisis temporal: Electrical Data
==================================================

Descomposición temporal para inv_01_dc_current_inv_149579:

Descomposición temporal para inv_01_dc_voltage_inv_149580:

<Figure size 1200x400 with 0 Axes>

<Figure size 1200x400 with 0 Axes>

<Figure size 1200x400 with 0 Axes>

<Figure size 1200x400 with 0 Axes>

==================================================
Análisis temporal: Environment Data
==================================================

Descomposición temporal para wind_direction_o_149577:

<Figure size 1200x400 with 0 Axes>

from utils import prepare_temporal_data

# 3.2 Preparación de datos para análisis posterior

electrical_data_prep = prepare_temporal_data(electrical_data_temp, "Electrical Data")
environment_data_prep = prepare_temporal_data(environment_data_temp, "Environment Data")
irradiance_data_prep = prepare_temporal_data(irradiance_data_temp, "Irradiance Data")

==================================================
Preparación de datos temporales: Electrical Data
==================================================

Completando valores faltantes...

Normalizando frecuencia temporal...
Frecuencia inferida: None
Usando frecuencia calculada: 300.0S

/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:306: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
  df_resampled = df_temp[numeric_cols].resample(freq).mean()

Extrayendo características temporales básicas...

/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:318: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_diff'] = df_resampled[col].diff()
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:319: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  df_resampled[f'{col}_pct_change'] = df_resampled[col].pct_change(fill_method=None)
/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:306: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
  df_resampled = df_temp[numeric_cols].resample(freq).mean()

Datos preparados con:
- Frecuencia normalizada: 300.0S
- Valores faltantes completados
- Características temporales añadidas
- Variables de diferencia calculadas

==================================================
Preparación de datos temporales: Environment Data
==================================================

Completando valores faltantes...

Normalizando frecuencia temporal...
Frecuencia inferida: None
Usando frecuencia calculada: 900.0S

Extrayendo características temporales básicas...

Datos preparados con:
- Frecuencia normalizada: 900.0S
- Valores faltantes completados
- Características temporales añadidas
- Variables de diferencia calculadas

==================================================
Preparación de datos temporales: Irradiance Data
==================================================

Completando valores faltantes...

Normalizando frecuencia temporal...
Frecuencia inferida: None
Usando frecuencia calculada: 300.0S

/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:306: FutureWarning: 'S' is deprecated and will be removed in a future version, please use 's' instead.
  df_resampled = df_temp[numeric_cols].resample(freq).mean()

Extrayendo características temporales básicas...

Datos preparados con:
- Frecuencia normalizada: 300.0S
- Valores faltantes completados
- Características temporales añadidas
- Variables de diferencia calculadas

from utils import detect_anomalies
# Electrical Data: Potencia de un inversor
electrical_data_anomalies = detect_anomalies(electrical_data_prep, 'inv_01_ac_power_inv_149583', method='isolation_forest')

# Environment Data: Temperatura ambiente
environment_data_anomalies = detect_anomalies(environment_data_prep, 'wind_direction_o_149577', method='lof')

# Irradiance Data: Irradiancia
print(irradiance_data_prep.head())
irradiance_data_anomalies = detect_anomalies(irradiance_data_prep, 'poa_irradiance_o_149574', method='zscore')

🔍 Método: ISOLATION_FOREST
Número de anomalías detectadas: 6327.0 (1.00%)
Parámetros usados: {'contamination': 0.01,'threshold': N/A}

🔍 Método: LOF
Número de anomalías detectadas: 28.0 (0.01%)
Parámetros usados: {'contamination': 0.01,'threshold': N/A}

                     poa_irradiance_o_149574  hour  day_of_week  day_of_month  \
measured_on                                                                     
2017-11-01 07:10:00                      0.0     7            2             1   
2017-11-01 07:15:00                      0.0     7            2             1   
2017-11-01 07:20:00                      0.0     7            2             1   
2017-11-01 07:25:00                      0.0     7            2             1   
2017-11-01 07:30:00                      NaN     7            2             1   

                     month  is_weekend  poa_irradiance_o_149574_diff  \
measured_on                                                            
2017-11-01 07:10:00     11           0                           NaN   
2017-11-01 07:15:00     11           0                           0.0   
2017-11-01 07:20:00     11           0                           0.0   
2017-11-01 07:25:00     11           0                           0.0   
2017-11-01 07:30:00     11           0                           NaN   

                     poa_irradiance_o_149574_pct_change  
measured_on                                              
2017-11-01 07:10:00                                 NaN  
2017-11-01 07:15:00                                 NaN  
2017-11-01 07:20:00                                 NaN  
2017-11-01 07:25:00                                 NaN  
2017-11-01 07:30:00                                 NaN

🔍 Método: ZSCORE
Número de anomalías detectadas: 48.0 (0.01%)
Parámetros usados: {'contamination': 0.01,'threshold': 3}

from utils import prophet_anomaly_detection
# 1. Sin regresores (solo la variable eléctrica)
prophet_electrical = prophet_anomaly_detection(electrical_data_prep, 'inv_01_ac_power_inv_149583', samples=1000)
prophet_environment = prophet_anomaly_detection(environment_data_prep, 'wind_direction_o_149577', samples=1000)
prophet_irradiance = prophet_anomaly_detection(irradiance_data_prep, 'poa_irradiance_o_149574', samples=1000)

11:18:22 - cmdstanpy - INFO - Chain [1] start processing

🔧 Entrenando modelo (esto puede tomar tiempo)...

11:18:23 - cmdstanpy - INFO - Chain [1] done processing

📊 Generando predicciones...
✅ Completado. Anomalías detectadas: 3

🔍 REPORTE COMPLETO DE ANOMALÍAS
==================================================

📊 RESUMEN GENERAL
Total de puntos analizados: 1000
Anomalías detectadas: 3 (0.30%)

📈 ESTADÍSTICAS DE ANOMALÍAS
Residual máximo: -7.63
Residual mínimo: -8.34
Desviación estándar promedio: -3.14

🚨 TOP 5 ANOMALÍAS MÁS GRANDES

⏳ DISTRIBUCIÓN TEMPORAL

Anomalías por hora del día:

/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:464: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

hour
8    3
Name: count, dtype: int64

Anomalías por día de la semana:

/home/chris/Documentos/python_projects/mineria_datos/proyecto/utils.py:469: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

weekday
Wednesday    3
Name: count, dtype: int64

	measured_on	inv_01_dc_current_inv_149579	inv_01_dc_voltage_inv_149580	inv_01_ac_current_inv_149581	inv_01_ac_voltage_inv_149582	inv_01_ac_power_inv_149583	inv_02_dc_current_inv_149584	inv_02_dc_voltage_inv_149585	inv_02_ac_current_inv_149586	inv_02_ac_voltage_inv_149587	inv_02_ac_power_inv_149588	inv_03_dc_current_inv_149589	inv_03_dc_voltage_inv_149590	inv_03_ac_current_inv_149591	inv_03_ac_voltage_inv_149592	inv_03_ac_power_inv_149593	inv_04_dc_current_inv_149594	inv_04_dc_voltage_inv_149595	inv_04_ac_current_inv_149596	inv_04_ac_voltage_inv_149597	inv_04_ac_power_inv_149598	inv_05_dc_current_inv_149599	inv_05_ac_current_inv_149601	inv_05_ac_voltage_inv_149602	inv_05_ac_power_inv_149603	inv_06_dc_current_inv_149604	inv_06_dc_voltage_inv_149605	inv_06_ac_current_inv_149606	inv_06_ac_voltage_inv_149607	inv_06_ac_power_inv_149608	inv_07_dc_current_inv_149609	inv_07_dc_voltage_inv_149610	inv_07_ac_current_inv_149611	inv_07_ac_voltage_inv_149612	inv_07_ac_power_inv_149613	inv_08_dc_current_inv_149614	inv_08_dc_voltage_inv_149615	inv_08_ac_current_inv_149616	inv_08_ac_voltage_inv_149617	inv_08_ac_power_inv_149618	inv_09_dc_current_inv_149619	inv_09_dc_voltage_inv_149620	inv_09_ac_current_inv_149621	inv_09_ac_voltage_inv_149622	inv_09_ac_power_inv_149623	inv_10_dc_current_inv_149624	inv_10_dc_voltage_inv_149625	inv_10_ac_current_inv_149626	inv_10_ac_voltage_inv_149627	inv_10_ac_power_inv_149628	inv_11_dc_current_inv_149629	inv_11_dc_voltage_inv_149630	inv_11_ac_current_inv_149631	inv_11_ac_voltage_inv_149632	inv_11_ac_power_inv_149633	inv_12_dc_current_inv_149634	inv_12_dc_voltage_inv_149635	inv_12_ac_current_inv_149636	inv_12_ac_voltage_inv_149637	inv_12_ac_power_inv_149638	inv_13_dc_current_inv_149639	inv_13_dc_voltage_inv_149640	inv_13_ac_current_inv_149641	inv_13_ac_voltage_inv_149642	inv_13_ac_power_inv_149643	inv_14_dc_current_inv_149644	inv_14_dc_voltage_inv_149645	inv_14_ac_current_inv_149646	inv_14_ac_voltage_inv_149647	inv_14_ac_power_inv_149648	inv_15_dc_current_inv_149649	inv_15_dc_voltage_inv_149650	inv_15_ac_current_inv_149651	inv_15_ac_voltage_inv_149652	inv_15_ac_power_iinv_149653	inv_16_dc_current_inv_149654	inv_16_dc_voltage_inv_149655	inv_16_ac_current_inv_149656	inv_16_ac_voltage_inv_149657	inv_16_ac_power_inv_149658	inv_17_dc_current_inv_149659	inv_17_dc_voltage_inv_149660	inv_17_ac_current_inv_149661	inv_17_ac_voltage_inv_149662	inv_17_ac_power_inv_149663	inv_18_dc_current_inv_149664	inv_18_dc_voltage_inv_149665	inv_18_ac_current_inv_149666	inv_18_ac_voltage_inv_149667	inv_18_ac_power_inv_149668	inv_19_dc_current_inv_149669	inv_19_dc_voltage_inv_149670	inv_19_ac_current_inv_149671	inv_19_ac_voltage_inv_149672	inv_19_ac_power_inv_149673	inv_20_dc_current_inv_149674	inv_20_dc_voltage_inv_149675	inv_20_ac_current_inv_149676	inv_20_ac_voltage_inv_149677	inv_20_ac_power_inv_149678	inv_21_dc_current_inv_149679	inv_21_dc_voltage_inv_149680	inv_21_ac_current_inv_149681	inv_21_ac_voltage_inv_149682	inv_21_ac_power_inv_149683	inv_22_dc_current_inv_149684	inv_22_dc_voltage_inv_149685	inv_22_ac_current_inv_149686	inv_22_ac_voltage_inv_149687	inv_22_ac_power_inv_149688	inv_23_dc_current_inv_149689	inv_23_dc_voltage_inv_149690	inv_23_ac_current_inv_149691	inv_23_ac_voltage_inv_149692	inv_23_ac_power_inv_149693	inv_24_dc_current_inv_149694	inv_24_dc_voltage_inv_149695	inv_24_ac_current_inv_149696	inv_24_ac_voltage_inv_149697	inv_24_ac_power_inv_149698
count	632952	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632586.000000	632952.000000	632952.000000	632952.000000	632952.000000	632952.000000	631224.000000	631224.000000	6.312240e+05	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	6.312240e+05	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	6.312240e+05	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.00000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.00000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000	631224.000000
unique	632952	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	2017-11-01 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	NaN	9.856498	319.205209	7.454513	136.362110	6.363338	9.228654	316.131588	6.988613	135.249976	5.982282	9.988669	330.187014	7.551154	145.218504	6.500057	9.336884	304.037668	1.481321e+05	132.865773	6.130343	9.840238	7.499344	138.567962	6.415813	7.224951e+04	301.613943	6.793178	130.691349	5.834427	8.898383	2.186520e+06	6.830158	127.442930	5.879789	10.144463	328.710893	7.720310	141.890221	6.564618	10.387627	336.468661	7.983449	146.715587	6.821695	10.280984	328.001489	7.812005	142.448472	6.64902	10.623234	333.753824	8.177335	145.354794	6.990957	10.369173	335.621148	7.954767	146.398971	6.829824	10.554597	338.963935	8.043897	145.087225	6.903008	10.157293	329.584876	7.830556	142.653776	6.681862	10.266145	329.817723	7.831213	144.259179	6.72965	10.005688	329.340681	7.638052	142.126222	6.537007	9.878494	324.686964	7.553521	139.201330	6.438483	10.250447	334.395949	7.780927	146.123299	6.717585	9.866144	333.206971	7.470421	147.162844	6.424750	10.706515	339.977851	8.209786	146.701772	7.049536	9.804948	334.120535	7.511014	143.407506	6.410051	10.397607	333.731323	7.970044	147.475516	6.891622	10.028295	332.493139	7.700779	142.157008	6.577954	9.691090	327.085068	7.449268	139.982626	6.382338
std	NaN	15.343311	337.317409	11.367359	143.161743	9.933949	14.928043	336.273826	10.973514	143.443901	9.607771	15.339447	334.748411	11.270075	144.960139	9.909333	15.152162	333.683578	1.176847e+08	143.867354	9.856842	15.321611	11.375410	142.926061	9.924226	5.739487e+07	334.856753	10.972734	143.742364	9.589361	14.891727	1.736950e+09	11.158162	143.129792	9.771875	15.703293	337.010445	11.590759	143.204191	10.089930	15.708819	336.542095	11.730733	144.598272	10.228337	15.790630	332.460844	11.652194	142.733681	10.12290	15.865384	335.553021	11.886606	143.180846	10.345037	15.546061	335.799193	11.645162	144.286116	10.165567	15.817337	338.306413	11.808397	144.022326	10.308671	15.628730	336.143930	11.674743	143.691883	10.183399	15.578807	335.386514	11.607848	144.374176	10.13432	15.385801	335.939452	11.411198	143.705472	9.968686	15.541239	337.158979	11.521450	143.156845	10.045097	15.489888	335.898679	11.457513	144.840569	10.073827	15.067273	334.048667	11.053540	144.806703	9.726843	15.867650	337.684988	11.867573	144.030676	10.391237	15.101541	339.753089	11.178806	143.603348	9.806287	15.587829	335.732962	11.614257	145.023864	10.244947	15.605688	338.846880	11.595841	143.247243	10.158701	15.295462	338.176004	11.389543	143.089715	10.000710
min	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	NaN	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.012000	292.489000	0.000000	267.740000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.059000	365.220000	0.000000	274.893000	0.000000	0.000000	290.267000	0.000000	9.492000	0.00000	0.000000	295.036000	0.000000	272.493000	0.000000	0.055000	360.026500	0.000000	274.743000	0.000000	0.137000	375.090500	0.000000	270.529000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	295.486000	0.000000	272.878000	0.000000	0.000000	296.772000	0.000000	275.343000	0.000000	0.085000	403.089000	0.000000	274.374000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	294.618000	0.000000	275.956000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	NaN	16.029000	675.261000	12.696000	286.213000	10.763000	13.966000	672.873000	11.175000	287.051000	9.538000	16.900000	675.077000	13.392000	289.901000	11.498000	13.982250	673.173000	1.132400e+01	288.043000	9.655000	16.287000	12.993000	285.753000	11.061250	1.268700e+01	672.072000	10.155000	288.095000	8.657250	12.233000	6.701010e+02	9.932000	287.208000	8.478000	17.018000	676.447000	13.509000	286.344000	11.482000	18.315250	679.437000	14.678000	289.136000	12.533000	17.596000	668.330000	13.966000	285.490000	11.90100	19.333000	680.255000	15.445000	286.514000	13.185000	18.623000	679.737000	14.838000	288.694000	12.717000	18.870000	681.574000	14.943000	288.199000	12.806000	17.475250	677.182000	14.070000	287.443000	12.005000	18.152000	677.533000	14.424000	288.668000	12.36500	17.189000	677.064000	13.739000	287.286000	11.722000	16.242000	675.562000	12.984000	286.265000	11.070000	18.120000	678.852000	14.346000	289.616000	12.341000	17.091000	675.144000	13.542000	289.603000	11.629000	19.472250	681.875000	15.524000	288.030000	13.286000	17.026250	680.793250	13.600000	287.064000	11.597000	18.652000	680.806000	14.845000	290.004000	12.773250	16.707000	680.205000	13.384000	286.370000	11.432250	15.544250	679.236000	12.518000	286.016000	10.718000
max	NaN	52.348000	909.840000	36.363000	310.677000	30.096000	54.844000	1750.755000	36.103000	310.508000	30.088000	55.819000	909.740000	36.218000	310.544000	30.092000	54.468000	1487.769000	9.350000e+10	347.127000	103.710000	53.569000	36.400000	310.036000	30.085000	4.560000e+10	912.095000	36.171000	311.329000	30.086000	54.274000	1.380000e+12	35.929000	311.498000	105.341000	54.730000	913.648000	36.514000	310.508000	30.089000	54.277000	921.714000	36.264000	310.448000	30.089000	55.843000	915.635000	36.538000	310.303000	30.08800	54.468000	912.462000	36.492000	310.218000	30.089000	54.959000	914.967000	36.140000	311.112000	30.089000	54.212000	912.412000	36.224000	310.436000	30.088000	53.665000	916.954000	36.397000	309.455000	30.091000	53.516000	916.036000	35.926000	310.544000	30.08600	53.721000	914.466000	35.910000	310.448000	30.086000	54.437000	914.683000	36.483000	310.605000	30.088000	54.046000	914.917000	35.869000	310.931000	30.086000	53.717000	914.165000	35.944000	310.387000	30.096000	55.095000	914.533000	36.249000	312.112000	30.095000	54.409000	925.054000	36.168000	310.617000	30.082000	53.762000	913.731000	35.678000	311.558000	30.093000	54.575000	913.080000	35.848000	310.822000	30.106000	55.051000	912.795000	36.066000	310.605000	30.089000

	measured_on	ambient_temperature_o_149575	wind_speed_o_149576	wind_direction_o_149577
0	2017-12-01 0:00:00	38.8	1.2	156.0
1	2017-12-01 0:15:00	38.8	1.2	156.0
2	2017-12-01 0:30:00	38.8	1.2	156.0
3	2017-12-01 0:45:00	38.8	1.2	156.0
4	2017-12-01 1:00:00	37.0	2.6	247.0

	measured_on	poa_irradiance_o_149574
count	531019	516584.000000
mean	2020-10-22 23:51:20.311250688	255.862654
min	2017-11-01 07:10:00	0.000000
25%	2019-05-03 11:52:30	0.000000
50%	2020-09-15 08:35:00	27.600000
75%	2022-04-15 09:27:30	503.500000
max	2023-11-01 23:55:00	1400.000000
std	NaN	341.748415

	Valores faltantes	Porcentaje (%)
inv_13_ac_voltage_inv_149642	1728	0.273006
inv_13_ac_power_inv_149643	1728	0.273006
inv_19_dc_current_inv_149669	1728	0.273006
inv_18_ac_power_inv_149668	1728	0.273006
inv_18_ac_voltage_inv_149667	1728	0.273006
...	...	...
inv_02_dc_voltage_inv_149585	366	0.057824
inv_02_ac_current_inv_149586	366	0.057824
inv_02_ac_voltage_inv_149587	366	0.057824
inv_02_ac_power_inv_149588	366	0.057824
inv_01_dc_current_inv_149579	366	0.057824

	count	mean	std	min	1%	5%	25%	50%	75%	95%	99%	max	IQR	Coef. Variación	Skewness	Kurtosis
inv_01_dc_current_inv_149579	632586.0	9.856498	15.343311	0.0	0.0	0.0	0.0	0.0	16.02900	43.543	48.148	52.348	16.02900	1.556670	1.327263	0.208461
inv_01_dc_voltage_inv_149580	632586.0	319.205209	337.317409	0.0	0.0	0.0	0.0	0.0	675.26100	729.021	755.558	909.840	675.26100	1.056742	0.140434	-1.935645
inv_01_ac_current_inv_149581	632586.0	7.454513	11.367359	0.0	0.0	0.0	0.0	0.0	12.69600	32.467	34.189	36.363	12.69600	1.524896	1.271537	0.030629
inv_01_ac_voltage_inv_149582	632586.0	136.362110	143.161743	0.0	0.0	0.0	0.0	0.0	286.21300	294.232	297.480	310.677	286.21300	1.049865	0.099705	-1.986636
inv_01_ac_power_inv_149583	632586.0	6.363338	9.933949	0.0	0.0	0.0	0.0	0.0	10.76300	28.332	29.939	30.096	10.76300	1.561122	1.293413	0.088258
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
inv_24_dc_current_inv_149694	631224.0	9.691090	15.295462	0.0	0.0	0.0	0.0	0.0	15.54425	43.149	47.634	55.051	15.54425	1.578302	1.335543	0.210588
inv_24_dc_voltage_inv_149695	631224.0	327.085068	338.176004	0.0	0.0	0.0	0.0	0.0	679.23600	738.023	810.203	912.795	679.23600	1.033908	0.113733	-1.914641
inv_24_ac_current_inv_149696	631224.0	7.449268	11.389543	0.0	0.0	0.0	0.0	0.0	12.51800	32.567	34.173	36.066	12.51800	1.528948	1.280345	0.046233
inv_24_ac_voltage_inv_149697	631224.0	139.982626	143.089715	0.0	0.0	0.0	0.0	0.0	286.01600	293.530	296.925	310.605	286.01600	1.022196	0.046172	-1.994632
inv_24_ac_power_inv_149698	631224.0	6.382338	10.000710	0.0	0.0	0.0	0.0	0.0	10.71800	28.543	29.957	30.089	10.71800	1.566935	1.297150	0.088821

Análisis exploratorio de Datos para Modelos de Pronóstico y Detección de Anomalías¶

Christopher Yahir Moreno Moreno¶

2.- Análisis descriptivo¶

Análisis Temporal¶

	measured_on	inv_01_dc_current_inv_149579	inv_01_dc_voltage_inv_149580	inv_01_ac_current_inv_149581	inv_01_ac_voltage_inv_149582	inv_01_ac_power_inv_149583	inv_02_dc_current_inv_149584	inv_02_dc_voltage_inv_149585	inv_02_ac_current_inv_149586	inv_02_ac_voltage_inv_149587	inv_02_ac_power_inv_149588	inv_03_dc_current_inv_149589	inv_03_dc_voltage_inv_149590	inv_03_ac_current_inv_149591	inv_03_ac_voltage_inv_149592	inv_03_ac_power_inv_149593	inv_04_dc_current_inv_149594	inv_04_dc_voltage_inv_149595	inv_04_ac_current_inv_149596	inv_04_ac_voltage_inv_149597	inv_04_ac_power_inv_149598	inv_05_dc_current_inv_149599	inv_05_ac_current_inv_149601	inv_05_ac_voltage_inv_149602	inv_05_ac_power_inv_149603	inv_06_dc_current_inv_149604	inv_06_dc_voltage_inv_149605	inv_06_ac_current_inv_149606	inv_06_ac_voltage_inv_149607	inv_06_ac_power_inv_149608	inv_07_dc_current_inv_149609	inv_07_dc_voltage_inv_149610	inv_07_ac_current_inv_149611	inv_07_ac_voltage_inv_149612	inv_07_ac_power_inv_149613	inv_08_dc_current_inv_149614	inv_08_dc_voltage_inv_149615	inv_08_ac_current_inv_149616	inv_08_ac_voltage_inv_149617	inv_08_ac_power_inv_149618	inv_09_dc_current_inv_149619	inv_09_dc_voltage_inv_149620	inv_09_ac_current_inv_149621	inv_09_ac_voltage_inv_149622	inv_09_ac_power_inv_149623	inv_10_dc_current_inv_149624	inv_10_dc_voltage_inv_149625	inv_10_ac_current_inv_149626	inv_10_ac_voltage_inv_149627	inv_10_ac_power_inv_149628	inv_11_dc_current_inv_149629	inv_11_dc_voltage_inv_149630	inv_11_ac_current_inv_149631	inv_11_ac_voltage_inv_149632	inv_11_ac_power_inv_149633	inv_12_dc_current_inv_149634	inv_12_dc_voltage_inv_149635	inv_12_ac_current_inv_149636	inv_12_ac_voltage_inv_149637	inv_12_ac_power_inv_149638	inv_13_dc_current_inv_149639	inv_13_dc_voltage_inv_149640	inv_13_ac_current_inv_149641	inv_13_ac_voltage_inv_149642	inv_13_ac_power_inv_149643	inv_14_dc_current_inv_149644	inv_14_dc_voltage_inv_149645	inv_14_ac_current_inv_149646	inv_14_ac_voltage_inv_149647	inv_14_ac_power_inv_149648	inv_15_dc_current_inv_149649	inv_15_dc_voltage_inv_149650	inv_15_ac_current_inv_149651	inv_15_ac_voltage_inv_149652	inv_15_ac_power_iinv_149653	inv_16_dc_current_inv_149654	inv_16_dc_voltage_inv_149655	inv_16_ac_current_inv_149656	inv_16_ac_voltage_inv_149657	inv_16_ac_power_inv_149658	inv_17_dc_current_inv_149659	inv_17_dc_voltage_inv_149660	inv_17_ac_current_inv_149661	inv_17_ac_voltage_inv_149662	inv_17_ac_power_inv_149663	inv_18_dc_current_inv_149664	inv_18_dc_voltage_inv_149665	inv_18_ac_current_inv_149666	inv_18_ac_voltage_inv_149667	inv_18_ac_power_inv_149668	inv_19_dc_current_inv_149669	inv_19_dc_voltage_inv_149670	inv_19_ac_current_inv_149671	inv_19_ac_voltage_inv_149672	inv_19_ac_power_inv_149673	inv_20_dc_current_inv_149674	inv_20_dc_voltage_inv_149675	inv_20_ac_current_inv_149676	inv_20_ac_voltage_inv_149677	inv_20_ac_power_inv_149678	inv_21_dc_current_inv_149679	inv_21_dc_voltage_inv_149680	inv_21_ac_current_inv_149681	inv_21_ac_voltage_inv_149682	inv_21_ac_power_inv_149683	inv_22_dc_current_inv_149684	inv_22_dc_voltage_inv_149685	inv_22_ac_current_inv_149686	inv_22_ac_voltage_inv_149687	inv_22_ac_power_inv_149688	inv_23_dc_current_inv_149689	inv_23_dc_voltage_inv_149690	inv_23_ac_current_inv_149691	inv_23_ac_voltage_inv_149692	inv_23_ac_power_inv_149693	inv_24_dc_current_inv_149694	inv_24_dc_voltage_inv_149695	inv_24_ac_current_inv_149696	inv_24_ac_voltage_inv_149697	inv_24_ac_power_inv_149698
0	2017-11-01 00:00:00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1	2017-11-01 00:05:00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2	2017-11-01 00:10:00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	2017-11-01 00:15:00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	2017-11-01 00:20:00	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	measured_on	ambient_temperature_o_149575	wind_speed_o_149576	wind_direction_o_149577
count	206008	205876	205992	206000.000000
unique	206008	836	272	NaN
top	2017-12-01 0:00:00	61.6	2.7	NaN
freq	1	616	4888	NaN
mean	NaN	NaN	NaN	187.551757
std	NaN	NaN	NaN	98.382701
min	NaN	NaN	NaN	0.000000
25%	NaN	NaN	NaN	123.000000
50%	NaN	NaN	NaN	162.000000
75%	NaN	NaN	NaN	270.000000
max	NaN	NaN	NaN	360.000000

	measured_on	poa_irradiance_o_149574
0	2017-11-01 07:10:00	0.0
1	2017-11-01 07:15:00	0.0
2	2017-11-01 07:20:00	0.0
3	2017-11-01 07:25:00	0.0
4	2017-11-01 09:00:00	267.5

	Valores cero	Valores infinitos
inv_01_dc_current_inv_149579	332145	0
inv_01_dc_voltage_inv_149580	329923	0
inv_01_ac_current_inv_149581	335279	0
inv_01_ac_voltage_inv_149582	329929	0
inv_01_ac_power_inv_149583	348575	0
...	...	...
inv_24_dc_current_inv_149694	322325	0
inv_24_dc_voltage_inv_149695	320692	0
inv_24_ac_current_inv_149696	333555	0
inv_24_ac_voltage_inv_149697	320695	0
inv_24_ac_power_inv_149698	353250	0

	Valores faltantes	Porcentaje (%)
ambient_temperature_o_149575	132	0.064075
wind_speed_o_149576	16	0.007767
wind_direction_o_149577	8	0.003883

	ds	yhat	residual	residual_std
104	2017-11-01 08:40:00	7.634967	-7.634967	-3.020069
105	2017-11-01 08:45:00	7.865792	-7.865792	-3.111374
107	2017-11-01 08:55:00	8.340238	-8.340238	-3.299044

	ds	y_true	yhat	residual	residual_std
911	2017-12-10 11:45:00	358.0	79.621006	278.378994	3.091858
495	2017-12-06 03:45:00	6.0	277.631787	-271.631787	-3.016919

	ds	y_true	yhat	residual	residual_std
367	2017-11-02 13:45:00	1031.8	663.687523	368.112477	3.463090
379	2017-11-02 14:45:00	848.7	495.111618	353.588382	3.326452
390	2017-11-02 15:40:00	671.8	326.969840	344.830160	3.244057
391	2017-11-02 15:45:00	651.6	312.835275	338.764725	3.186995
393	2017-11-02 15:55:00	622.7	285.490534	337.209466	3.172364